一种用于预测重复前列腺活检结果的人工神经网络。

An artificial neural network to predict the outcome of repeat prostate biopsies.

作者信息

Remzi Mesut, Anagnostou Theodore, Ravery Vincent, Zlotta Alexandre, Stephan Carsten, Marberger Michael, Djavan Bob

机构信息

Department of Urology, University of Vienna, Vienna, Austria.

出版信息

Urology. 2003 Sep;62(3):456-60. doi: 10.1016/s0090-4295(03)00409-6.

DOI:10.1016/s0090-4295(03)00409-6

PMID:12946746

Abstract

OBJECTIVES

To develop an advanced artificial neural network (ANN) to predict the presence of prostate cancer (PCa) and to predict the outcome of repeat prostate biopsies. The predictive accuracy was compared with the accuracy obtained using standard cutoffs for the free/total (f/t) prostate-specific antigen (PSA) ratio, PSA density (PSAD), PSA density of the transition zone (PSA-TZ), and the total and transition zone volumes. Clinical and biochemical diagnostic tests have been shown to improve PCa detection. When these tests are combined using an ANN, significant increases in specificity at high sensitivity are observed.

METHODS

The Vienna-based multicenter European referral database for early PCa detection of 820 men with a PSA level between 4 and 10 ng/mL was used. The presence of PCa was determined using transrectal ultrasound-guided octant needle repeat biopsy. Variables in the database consisted of age, PSA, f/t PSA ratio, digital rectal examination findings, PSA velocity, and the transrectal ultrasound-guided variables of prostate volume, transition zone volume, PSAD, and PSA-TZ. The ANN used in the analysis was an advanced multilayer perceptron selected for accuracy by a genetic algorithm.

RESULTS

The repeat biopsy PCa detection rate was 10% (n = 83). At 95% sensitivity, the specificity for ANN was 68% compared with 54%, 33.5%, 21.4%, 14.7%, and 8.3% for multivariate logistic regression analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively. The ANN reduced unnecessary repeat biopsies by 68% in this study. The area under the curve was 83% for the ANN versus 79%, 74.5%, 69.1%, 61.8%, and 60.5% for multivariate analysis, f/t PSA ratio, PSA-TZ, PSAD, and total PSA, respectively.

CONCLUSIONS

The current ANN found a strong pattern predictive of PCa in patients with a negative initial biopsy. By combining the individual clinical and biochemical markers into the ANN, 68% specificity at 95% sensitivity was achieved. The ANN allows more accurate and individual counseling of patients with a negative initial biopsy.

摘要

目的

开发一种先进的人工神经网络（ANN），以预测前列腺癌（PCa）的存在，并预测重复前列腺活检的结果。将预测准确性与使用游离/总（f/t）前列腺特异性抗原（PSA）比值、PSA密度（PSAD）、移行区PSA密度（PSA-TZ）以及总体积和移行区体积的标准临界值所获得的准确性进行比较。临床和生化诊断测试已被证明可改善PCa检测。当使用人工神经网络将这些测试结合起来时，在高灵敏度下特异性会显著提高。

方法

使用基于维也纳的多中心欧洲早期PCa检测转诊数据库，该数据库包含820名PSA水平在4至10 ng/mL之间的男性。通过经直肠超声引导的八分法针重复活检确定是否存在PCa。数据库中的变量包括年龄、PSA、f/t PSA比值、直肠指检结果、PSA速度以及经直肠超声引导的前列腺体积、移行区体积、PSAD和PSA-TZ变量。分析中使用的人工神经网络是一种通过遗传算法选择以提高准确性的先进多层感知器。

结果

重复活检的PCa检出率为10%（n = 83）。在95%的灵敏度下，人工神经网络的特异性为68%，而多变量逻辑回归分析、f/t PSA比值、PSA-TZ、PSAD和总PSA的特异性分别为54%、33.5%、21.4%、14.7%和8.3%。在本研究中，人工神经网络减少了68%的不必要重复活检。人工神经网络的曲线下面积为83%，而多变量分析、f/t PSA比值、PSA-TZ、PSAD和总PSA的曲线下面积分别为79%、74.5%、69.1%、61.8%和60.5%。